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A Machine Learning Approach for Game Bot Detection Through Behavioural Features

  • Mario Luca Bernardi
  • Marta CimitileEmail author
  • Fabio Martinelli
  • Francesco Mercaldo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 868)

Abstract

In the last years, online games market has been interested by a sudden growth due to the birth of new gaming infrastructures that offer more effective and innovative services and products. Simultaneously to the diffusion of on line games, there was an increasing use of game bots to automatically perform malicious tasks. Game bots users aim to obtain some rewards by automating the most tedious and prolonged activities arousing the disappointment of the game community. Therefore, the detection and the expulsion of game bots from the game environment, become critical issues for the game’s developers that want to ensure the satisfaction of all the players. This paper describes an approach for the game bot detection in the online role player games consisting to distinguish between game bots and human behavior and based on the adoption of supervised and unsupervised machine learning techniques. These techniques are used to discriminate between users and game bots basing on some user behavioral features. The approach is applied to a real-world dataset of a popular role player game and the obtained results are encouraging.

Keywords

Game bot Machine learning Cluster analysis Game bot detection Security Testing 

Notes

Acknowledgements

This work has been partially supported by H2020 EU-funded projects NeCS and C3ISP and EIT-Digital Project HII.

References

  1. 1.
    Adams, E.: Fundamentals of Game Design (2014)Google Scholar
  2. 2.
    Quandt, T., Kröger, S.: Multiplayer: The Social Aspects of Digital Gaming, vol. 3. Routledge, Abingdon (2013)Google Scholar
  3. 3.
    Seay, A.F., Jerome, W.J., Lee, K.S., Kraut, R.E.: Project massive: a study of online gaming communities. In: CHI 2004 Extended Abstracts on Human Factors in Computing Systems, pp. 1421–1424. ACM (2004)Google Scholar
  4. 4.
    Yee, N.: Maps of digital desires: exploring the topography of gender and play in online games. In: Beyond Barbie and Mortal Kombat: New Perspectives on Gender and Gaming, pp. 83–89 (2008)Google Scholar
  5. 5.
    Griffiths, M.D., Davies, M.N., Chappell, D.: Online computer gaming: a comparison of adolescent and adult gamers. J. Adolesc. 27, 87–96 (2004)CrossRefGoogle Scholar
  6. 6.
    Chen, Y.C., Chen, P.S., Song, R., Korba, L.: Online gaming crime and security issue-cases and countermeasures from Taiwan. In: PST, pp. 131–136 (2004)Google Scholar
  7. 7.
    Paulson, R.A., Weber, J.E.: Cyberextortion: an overview of distributed denial of service attacks against online gaming companies. Issues Inf. Syst. 7, 52–56 (2006)Google Scholar
  8. 8.
    Yampolskiy, R.V., Govindaraju, V.: Embedded noninteractive continuous bot detection. Comput. Entertain. (CIE) 5, 7 (2008)Google Scholar
  9. 9.
    Kang, A.R., Jeong, S.H., Mohaisen, A., Kim, H.K.: Multimodal game bot detection using user behavioral characteristics. SpringerPlus 5, 523 (2016)CrossRefGoogle Scholar
  10. 10.
    Cabello, E., Cardoso, J., Ludwig, A., Maciaszek, L.A., van Sinderen, M. (eds.): ICSOFT 2016. CCIS, vol. 743. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-62569-0CrossRefGoogle Scholar
  11. 11.
    Bernardi, M.L., Cimitile, M., Mercaldo, F.: A time series classification approach to game bot detection. In: Proceeding of the 7th ACM International Conference on Web Intelligence, Mining and Semantics, pp. 512–519 (2017)Google Scholar
  12. 12.
    Varvello, M., Voelker, G.M.: Second life: a social network of humans and bots. In: Proceedings of the 20th International Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2010, pp. 9–14. ACM, New York (2010)Google Scholar
  13. 13.
    Oh, J., Borbora, Z.H., Sharma, D., Srivastava, J.: Bot detection based on social interactions in MMORPGs. In: 2013 International Conference on Social Computing (SocialCom), pp. 536–543. IEEE (2013)Google Scholar
  14. 14.
    Kang, A.R., Woo, J., Park, J., Kim, H.K.: Online game bot detection based on party-play log analysis. Comput. Math. Appl. 65, 1384–1395 (2013)CrossRefGoogle Scholar
  15. 15.
    Kim, H., Hong, S., Kim, J.: Detection of auto programs for MMORPGs. In: Zhang, S., Jarvis, R. (eds.) AI 2005. LNCS (LNAI), vol. 3809, pp. 1281–1284. Springer, Heidelberg (2005).  https://doi.org/10.1007/11589990_187CrossRefGoogle Scholar
  16. 16.
    Chen, K.T., Pao, H.K.K., Chang, H.C.: Game bot identification based on manifold learning. In: Proceedings of the 7th ACM SIGCOMM Workshop on Network and System Support for Games, pp. 21–26. ACM (2008)Google Scholar
  17. 17.
    Chen, K.-T., Liao, A., Pao, H.-K.K., Chu, H.-H.: Game bot detection based on avatar trajectory. In: Stevens, S.M., Saldamarco, S.J. (eds.) ICEC 2008. LNCS, vol. 5309, pp. 94–105. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-89222-9_11CrossRefGoogle Scholar
  18. 18.
    Chen, K.T., Jiang, J.W., Huang, P., Chu, H.H., Lei, C.L., Chen, W.C.: Identifying MMORPG bots: a traffic analysis approach. In: Proceedings of the 2006 ACM SIGCHI International Conference on Advances in Computer Entertainment Technology, ACE 2006. ACM, New York (2006)Google Scholar
  19. 19.
    Kwon, H., Mohaisen, A., Woo, J., Kim, Y., Lee, E., Kim, H.K.: Crime scene reconstruction: online gold farming network analysis. IEEE Trans. Inf. Forensics Secur. 12, 544–556 (2017)Google Scholar
  20. 20.
    Kim, H., Yang, S., Kim, H.K.: Crime scene re-investigation: a postmortem analysis of game account stealers’ behaviors. CoRR abs/1705.00242 (2017)Google Scholar
  21. 21.
    Thawonmas, R., Kashifuji, Y., Chen, K.T.: Detection of MMORPG bots based on behavior analysis. In: Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology, pp. 91–94. ACM (2008)Google Scholar
  22. 22.
    Kashifuji, Y.: Detection of MMORPG bots based on behavior analysis. In: ACE 2008 (2008)Google Scholar
  23. 23.
    Hilaire, S., Kim, H., Kim, C.: How to deal with bot scum in MMORPGs? In: 2010 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR), pp. 1–6. IEEE (2010)Google Scholar
  24. 24.
    Mishima, Y., Fukuda, K., Esaki, H.: An analysis of players and bots behaviors in MMORPG. In: 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA), pp. 870–876. IEEE (2013)Google Scholar
  25. 25.
    Chung, Y., Park, C.Y., Kim, N., Cho, H., Yoon, T.B., Lee, H., Lee, J.: A behavior analysis-based game bot detection approach considering various play styles. CoRR abs/1509.02458 (2015)Google Scholar
  26. 26.
    Mitchell, T.M.: Machine learning and data mining. Commun. ACM 42, 30–36 (1999)CrossRefGoogle Scholar
  27. 27.
    Michalski, R.S., Carbonell, J.G., Mitchell, T.M.: Machine Learning: An Artificial Intelligence Approach. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-662-12405-5CrossRefzbMATHGoogle Scholar
  28. 28.
    Estai, M., Kanagasingam, Y., Xiao, D., Vignarajan, J., Bunt, S., Kruger, E., Tennant, M.: End-user acceptance of a cloud-based teledentistry system and Android phone app for remote screening for oral diseases. J. Telemed. Telecare 23, 44–52 (2017)CrossRefGoogle Scholar
  29. 29.
    Canfora, G., De Lorenzo, A., Medvet, E., Mercaldo, F., Visaggio, C.A.: Effectiveness of opcode ngrams for detection of multi family Android malware. In: 2015 10th International Conference on Availability, Reliability and Security (ARES), pp. 333–340. IEEE (2015)Google Scholar
  30. 30.
    Canfora, G., Mercaldo, F., Visaggio, C.A.: A classifier of malicious Android applications. In: 2013 Eighth International Conference on Availability, Reliability and Security (ARES), pp. 607–614. IEEE (2013)Google Scholar
  31. 31.
    Ling, C.X., Yang, Q., Wang, J., Zhang, S.: Decision trees with minimal costs. In: Proceedings of the Twenty-First International Conference on Machine learning, p. 69. ACM (2004)Google Scholar
  32. 32.
    Jin, C., De-Lin, L., Fen-Xiang, M.: An improved ID3 decision tree algorithm. In: 4th International Conference on Computer Science and Education, ICCSE 2009, pp. 127–130. IEEE (2009)Google Scholar
  33. 33.
    Pang, J., Huang, Q., Jiang, S.: Multiple instance boost using graph embedding based decision stump for pedestrian detection. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5305, pp. 541–552. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-88693-8_40CrossRefGoogle Scholar
  34. 34.
    Hang, Y., Fong, S.: Investigating the impact of bursty traffic on Hoeffding Tree Algorithm in stream mining over internet. In: 2010 Second International Conference on Evolving Internet (INTERNET), pp. 147–152. IEEE (2010)Google Scholar
  35. 35.
    Liaw, A., Wiener, M., et al.: Classification and regression by randomForest. R News 2, 18–22 (2002)Google Scholar
  36. 36.
    Cutler, A., Zhao, G.: Pert-perfect random tree ensembles. Comput. Sci. Stat. 33, 490–497 (2001)Google Scholar
  37. 37.
    Zhao, Y., Zhang, Y.: Comparison of decision tree methods for finding active objects. Adv. Space Res. 41, 1955–1959 (2008)CrossRefGoogle Scholar
  38. 38.
    Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C., Silverman, R., Wu, A.Y.: The analysis of a simple k-means clustering algorithm. In: Proceedings of the Sixteenth Annual Symposium on Computational Geometry, pp. 100–109. ACM (2000)Google Scholar
  39. 39.
    Kumar, M., et al.: An optimized farthest first clustering algorithm. In: 2013 Nirma University International Conference on Engineering (NUiCONE), pp. 1–5. IEEE (2013)Google Scholar
  40. 40.
    Panda, M., Patra, M.: A novel classification via clustering method for anomaly based network intrusion detection system. Int. J. Recent Trends Eng. 2, 1–6 (2009)Google Scholar
  41. 41.
    Pandey, A.K., Pandey, P., Jaiswal, K., Sen, A.K.: Datamining clustering techniques in the prediction of heart disease using attribute selection method. Heart Dis. 14, 16–17 (2013)Google Scholar
  42. 42.
    Fisher, D.H.: Knowledge acquisition via incremental conceptual clustering. Mach. Learn. 2, 139–172 (1987)Google Scholar
  43. 43.
    Dua, S., Du, X.: Data Mining and Machine Learning in Cybersecurity. CRC Press, Boca Raton (2016)zbMATHGoogle Scholar
  44. 44.
    Bernardi, M.L., Cimitile, M., Distante, D., Mercaldo, F.: Game bot detection in online role player game through behavioural features. In: Proceeding of the 12th International Conference on Software Technologies (2017)Google Scholar
  45. 45.
    Bernardi, M.L., Cimitile, M., Di Francescomarino, C., Maggi, F.M.: Do activity lifecycles affect the validity of a business rule in a business process? Inf. Syst. 62, 42–59 (2016)CrossRefGoogle Scholar
  46. 46.
    Francesco, N.D., Lettieri, G., Santone, A., Vaglini, G.: Heuristic search for equivalence checking. Softw. Syst. Model. 15, 513–530 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mario Luca Bernardi
    • 1
  • Marta Cimitile
    • 2
    Email author
  • Fabio Martinelli
    • 3
  • Francesco Mercaldo
    • 3
  1. 1.Giustino Fortunato UniversityBeneventoItaly
  2. 2.Unitelma SapienzaRomaItaly
  3. 3.Institute for Informatics and TelematicsNational Research Council of Italy (CNR)PisaItaly

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